I’m using jupyter notebook in vscode and running a virtual enviroment with python 3.10.12.

The python script is as follow:

import math
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers

def get_model():
    SAMPLES = 1000
    np.random.seed(1337)
    x_values = np.random.uniform(low=0, high=2*math.pi, size=SAMPLES)
    # shuffle and add noise
    np.random.shuffle(x_values)
    y_values = np.sin(x_values)
    y_values += 0.1 * np.random.randn(*y_values.shape)

    # split into train, validation, test
    TRAIN_SPLIT =  int(0.6 * SAMPLES)
    TEST_SPLIT = int(0.2 * SAMPLES + TRAIN_SPLIT)
    x_train, x_test, x_validate = np.split(x_values, [TRAIN_SPLIT, TEST_SPLIT])
    y_train, y_test, y_validate = np.split(y_values, [TRAIN_SPLIT, TEST_SPLIT])

    # create a NN with 2 layers of 16 neurons
    model = tf.keras.Sequential()
    model.add(layers.Dense(16, activation='relu', input_shape=(1,)))
    model.add(layers.Dense(16, activation='relu'))
    model.add(layers.Dense(1))
    model.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])
    model.fit(x_train, y_train, epochs=200, batch_size=16,
                        validation_data=(x_validate, y_validate))
    return model

model = get_model()
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]
tflite_model = converter.convert()   #evertime it crashes in here

# Save the model to disk
open("sine_model_quantized.tflite", "wb").write(tflite_model)

The requirements.txt file contains:

numpy
tensorflow
keras

The output msg is as follow:

23:43:33.832 [error] Disposing session as kernel process died ExitCode: undefined, Reason: 2024-06-27 23:43:02.835210: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-06-27 23:43:02.890001: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-06-27 23:43:03.733460: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
2024-06-27 23:43:11.116507: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-06-27 23:43:11.155100: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2251] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.
Skipping registering GPU devices...

I try it in google colab and it works fine, but crashes when I try to run it in local. The laptop has a rtx 2050 graph card, but it’s not cuda capable so I just want to run it with CPU.

I’m appraciate if anyone could offer some help. Thanks!